A novel additive regression model for streamflow forecasting in German rivers

Results in Engineering, Journal Year: 2024, Volume and Issue: 22, P. 102104 - 102104

Published: April 10, 2024

Forecasting streamflows, essential for flood mitigation and the efficient management of water resources drinking, agriculture hydroelectric power generation, presents a formidable challenge in most real-world scenarios. In this study, two models, first based on Additive Regression Radial Basis Function Neural Networks (AR-RBF) second stacking with Pace Multilayer Perceptron Random Forest (MLP-RF-PR), were compared prediction short-term (1–3 days ahead) medium-term (7 daily streamflow rates three different rivers Germany: Elbe River at Wittenberge, Leine Herrenhausen, Saale Hof The lagged values rate, precipitation temperature considered modeling. Moreover, Bayesian Optimization (BO) algorithm was used to assess optimal number hyperparameters. Both models showed accurate predictions forecasting, R2 1-day ahead ranging from 0.939 0.998 AR-RBF 0.930 0.996 MLP-RF-PR, while MAPE ranged 2.02 % 8.99 2.14 9.68 when exogeneous variables included. As forecast horizon increased, reduction forecasting accuracy observed. However, both could still predict overall flow pattern, even 7-day-ahead predictions, 0.772 0.871 0.703 0.840 10.60 20.45 10.44 19.65 MLP-RF-PR. Overall, outcomes study suggest that MLP-RF-PR can be reliable tools short- rate prediction, requiring short parameters optimized, making them easy implement reducing calculation time required.

Language: Английский

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Revolutionizing the Future of Hydrological Science: Impact of Machine Learning and Deep Learning amidst Emerging Explainable AI and Transfer Learning

Applied Computing and Geosciences, Journal Year: 2024, Volume and Issue: 24, P. 100206 - 100206

Published: Nov. 9, 2024

Language: Английский

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Enhancing hydrological time series forecasting with a hybrid Bayesian-ConvLSTM model optimized by particle swarm optimization

Acta Geophysica, Journal Year: 2025, Volume and Issue: unknown

Published: March 24, 2025

Language: Английский

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Development and optimization of geopolymer concrete with compressive strength prediction using particle swarm-optimized extreme gradient boosting

Applied Soft Computing, Journal Year: 2025, Volume and Issue: unknown, P. 113149 - 113149

Published: April 1, 2025

Language: Английский

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Improved streamflow prediction accuracy in Boreal climate watershed using a LSTM model: A comparative study

PLOS Water, Journal Year: 2025, Volume and Issue: 4(4), P. e0000359 - e0000359

Published: April 21, 2025

Streamflow plays a vital role in water resource management and environmental impact assessment. This study is novel application of the Long Short-Term Memory (LSTM) model, type recurrent neural network, for real-time streamflow prediction Upper Humber River Watershed western Newfoundland. It also compares performance LSTM model with physically based SWAT model. The was optimized by tuning hyperparameters adjusting window size to balance capturing historical data ensuring stability. Using single input variables such as daily average temperature or precipitation, achieved high Nash-Sutcliffe Efficiency (NSE) 0.95. In comparison, results show that delivers more competitive performance, achieving an NSE 0.95 versus SWAT’s 0.77, percent bias (PBIAS) 0.62 compared 8.26. Unlike SWAT, does not overestimate flows excels predicting low flows. Additionally, successfully predicted using data. Despite challenges interpretability generalizability, demonstrated strong particularly during extreme events, making it valuable tool cold climates where accurate forecasts are crucial effective management. highlights potential model’s

Language: Английский

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Ensemble deep learning techniques for time series analysis: a comprehensive review, applications, open issues, challenges, and future directions

Cluster Computing, Journal Year: 2024, Volume and Issue: 28(1)

Published: Nov. 8, 2024

Language: Английский

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A hybrid model of ARIMA and MLP with a Grasshopper optimization algorithm for time series forecasting of water quality

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 13, 2024

Water quality monitoring of rivers is necessary in order to properly manage their basins so that steps can be taken control the amount pollutants and bring them allowable level. The ARIMA (autoregressive integrated moving average) model does not consider nonlinear patterns modeling water components. Also, using MLP (Multilayer Perceptrons) model, both linear pattern are controlled equally. Therefore, present study, time series models (ARIMA), a hybrid optimized by Grasshopper optimization algorithm used predict components statistical period 2011–2019. In proposed method, ability exploited. Observational data for forecasting method include dissolved oxygen, temperature, boron over 108 months. Since, capable realizing essence complicated series, it makes more reliable forecasts. correlation coefficients between observational predicted values 0.9 0.91 boron. To compare three ARIMA, MLP, models, accuracy indices each calculated. results show model's higher compared with other two models.

Language: Английский

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Applying Data-Driven Modeling for Streamflow Prediction in Semi-Arid Watersheds: A Comparative Evaluation of Machine Learning and Deep Learning Methodologies

Pure and Applied Geophysics, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 13, 2024

Language: Английский

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